---
library_name: transformers
tags:
- vit
- cifar10
- image classification
license: apache-2.0
datasets:
- uoft-cs/cifar10
language:
- en
metrics:
- accuracy
- perplexity
pipeline_tag: image-classification
---


## Model Details

### Model Description

An adapter for the [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) ViT trained on CIFAR10 classification task

## Loading guide

```py
from transformers import AutoModelForImageClassification

labels2title = ['plane', 'car', 'bird', 'cat',
    'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
model = AutoModelForImageClassification.from_pretrained(
    'google/vit-base-patch16-224-in21k',
    num_labels=len(labels2title),
    id2label={i: c for i, c in enumerate(labels2title)},
    label2id={c: i for i, c in enumerate(labels2title)}
)
model.load_adapter("yturkunov/cifar10_vit16_lora")
```

## Learning curves

![image/png](https://cdn-uploads.huggingface.co/production/uploads/655221be7bd4634260e032ca/Ji1ewA_8T1rJuQkdNCIXQ.png)

### Recommendations to input
The model expects an image that has went through the following preprocessing stages:
* Scaling range:
  <img src="https://latex.codecogs.com/gif.latex?[0, 255]\rightarrow[0, 1]" />
* Normalization parameters:
  <img src="https://latex.codecogs.com/gif.latex?\mu=(.5,.5,.5),\sigma=(.5,.5,.5)" />
* Dimensions: 224x224
* Number of channels: 3

### Inference on 3x4 random sample

![image/png](https://cdn-uploads.huggingface.co/production/uploads/655221be7bd4634260e032ca/zxj9ID37gJJnkmc8Sl97A.png)